Instructions to use radned/model1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radned/model1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="radned/model1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("radned/model1") model = AutoModelForSequenceClassification.from_pretrained("radned/model1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tutorial.py from radned/model1: direct link, hf CLI and curl.
- Browser
- Download file 615 Bytes
-
https://huggingface.co/radned/model1/resolve/main/tutorial.py
- Command line
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hf download hf://radned/model1/tutorial.py
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curl -L -o tutorial.py https://huggingface.co/radned/model1/resolve/main/tutorial.py
615 Bytes
| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # # New Chapter | |
| # ## Section | |
| # * item *italicised* and **bold** | |
| # In[3]: | |
| def f(x): | |
| return x**2 | |
| # In[2]: | |
| f(3) | |
| # In[4]: | |
| get_ipython().system('jupyter notebook list') | |
| # In[5]: | |
| get_ipython().run_line_magic('lsmagic', '') | |
| # In[6]: | |
| get_ipython().system('pwd') | |
| # In[7]: | |
| get_ipython().run_line_magic('pwd', '') | |
| # In[8]: | |
| pwd | |
| # In[14]: | |
| get_ipython().run_cell_magic('timeit', '', 'for i in range(1000):\n i**2\n') | |
| # In[11]: | |
| get_ipython().run_cell_magic('latex', '', '\\begin{equation}\n x = \\frac{1}{q}\n\\end{equation} \n') | |
| # In[ ]: | |